提出了一种生成软糖手指的新方法。描述了一个中型的假指纹数据库，并在其上评估了两个不同的指纹验证系统。实验中考虑了三种不同的情况，即：使用真实的指纹注册和测试，用假指纹进行注册和测试，以及带有真实指纹的注册，并用假指纹进行测试。给出了光学和热扫描传感器的结果。两种系统都被证明容易受到直接攻击。

提出了基于质量度量的LIVISE检测的新指纹参数化。新颖的功能集用于完整的LIVESTECTY检测系统中，并在Livdet竞争的开发集中进行了测试，其中包括具有三个不同光学传感器的4,500多个真实图像和假图像。提出的解决方案证明对多传感器方案是可靠的，并且总体率是正确分类的样品的93％。此外，提出的LIVISE检测方法比先前研究的技术具有额外的优势，即仅需要一个图像从手指决定是真实还是假货。

虹膜识别技术在过去几十年中吸引了日益增长的兴趣，我们目睹了从研究实验室迁移到现实世界应用的迁移。该技术的部署提出了关于与这些系统相关的主要漏洞和安全威胁的问题。在这些威胁中，介绍攻击突出了一些最相关和研究的。呈现攻击可以被定义为人类特征或工件的呈现直接到试图干扰其正常操作的生物识别系统的捕获设备。在虹膜的情况下，这些攻击包括使用真正的虹膜以及具有不同级别的复杂程度的工件，例如照片或视频。本章介绍了已开发的虹膜演示攻击检测（PAD）方法，以降低呈现攻击所带来的风险。首先，我们总结了最受欢迎的攻击类型，包括地址的主要挑战。其次，我们提出了一个介绍攻击检测方法的分类，作为这一非常活跃的研究区域的简要介绍。最后，我们讨论了这些方法根据实际应用中最重要的情况识别虹膜识别系统。

本章的主要范围是作为面部介绍攻击检测的介绍，包括过去几年的关键资源和领域的进步。下一页呈现了面部识别系统可以面对的不同演示攻击，其中攻击者向传感器提供给传感器，主要是相机，呈现攻击仪器（PAI），这通常是照片，视频或掩码，试图冒充真正的用户。首先，我们介绍了面部识别的现状，部署水平及其挑战。此外，我们介绍了面部识别系统可能暴露的漏洞和可能的攻击，表明呈现攻击检测方法的高度重要性。我们审核不同类型的演示攻击方法，从更简单到更复杂，在哪个情况下它们可能是有效的。然后，我们总结了最受欢迎的演示文稿攻击检测方法来处理这些攻击。最后，我们介绍了研究界使用的公共数据集，以探索面部生物识别性的脆弱性，以呈现攻击，并对已知的PAI制定有效的对策。

展示了在欧洲生物安全卓越网络框架内设计和获取的新的多模态生物识别数据库。它由600多个个人在三种情况下在三种情况下获得：1）在互联网上，2）在带台式PC的办公环境中，以及3）在室内/室外环境中，具有移动便携式硬件。这三种方案包括音频/视频数据的共同部分。此外，已使用桌面PC和移动便携式硬件获取签名和指纹数据。此外，使用桌面PC在第二个方案中获取手和虹膜数据。收购事项已于11名欧洲机构进行。 BioSecure多模式数据库（BMDB）的其他功能有：两个采集会话，在某些方式的几种传感器，均衡性别和年龄分布，多式化现实情景，每种方式，跨欧洲多样性，人口统计数据的可用性，以及人口统计数据的可用性与其他多模式数据库的兼容性。 BMDB的新型收购条件允许我们对单币或多模式生物识别系统进行新的具有挑战性的研究和评估，如最近的生物安全的多模式评估活动。还给出了该活动的描述，包括来自新数据库的单个模式的基线结果。预计数据库将通过2008年通过生物安全协会进行研究目的

提出了一种使用基于质量相关特征的新颖的指纹参数化的新的基于软件的活性检测方法。该系统在高度挑战的数据库上测试，该数据库包括超过10,500个实际和假图像，其中包含不同技术的五个传感器，并在材料和程序中覆盖各种直接攻击情景，然后遵循生成胶状手指。所提出的解决方案证明对多场景数据集具有强大，并呈现90％正确分类的样本的总速率。此外，所呈现的活性检测方法具有上述从手指中仅需要一个图像的先前研究的技术的额外优点，以决定它是真实还是假的。最后一个特征提供了具有非常有价值的功能的方法，因为它使其更不具有侵入性，更多的用户友好，更快，并降低其实现成本。

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

In this paper, we present an evolved version of the Situational Graphs, which jointly models in a single optimizable factor graph, a SLAM graph, as a set of robot keyframes, containing its associated measurements and robot poses, and a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between those elements. Our proposed S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robot's pose and its map, simultaneously constructing and leveraging the high-level information of the environment. To extract such high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets including, simulations of distinct indoor environments, on real datasets captured over several construction sites and office environments, and on a real public dataset of indoor office environments. S-Graphs+ outperforms relevant baselines in the majority of the datasets while extending the robot situational awareness by a four-layered scene model. Moreover, we make the algorithm available as a docker file.

The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.